Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
608
datasets available to search
ShareScore release 0.9.0
Dataset results
608 results for “ensembles”
Heterogeneous Habenular Neuronal Ensembles during Selection of Defensive Behaviors
<p>Optimal selection of threat-driven defensive behaviors is paramount to an animal's survival. The lateral habenula (LHb) is a key neuronal hub coordinating behavioral responses to aversive stimuli. Yet, how individual LHb neurons represent defensive behaviors in response to threats remains unknown. Here, we show that in mice, a visual threat promotes distinct defensive behaviors, namely runaway (escape) and action-locking (immobile-like). Fiber photometry of bulk LHb neuronal activity in behaving animals reveals an increase and a decrease in calcium signal time-locked with runaway and action-locking, respectively. Imaging single-cell calcium dynamics across distinct threat-driven behaviors identify independently active LHb neuronal clusters. These clusters participate during specific time epochs of defensive behaviors. Decoding analysis of this neuronal activity reveals that some LHb clusters either predict the upcoming selection of the defensive action or represent the selected action. Thus, heterogeneous neuronal clusters in LHb predict or reflect the selection of distinct threat-driven defensive behaviors.</p>
Data of the publication Optimizing ion implantation to create shallow NV centre ensembles in high-quality CVD diamond
<p>Data of the publication published by IOP under the reference: Midrel Wilfried Ngandeu Ngambou <em>et al</em> 2022 <em>Mater. Quantum. Technol.</em> <strong>2</strong> 045001 .</p>
CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
<p>CREMP: A resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical tight-binding DFT calculations. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.</p> <p>We provide the data in two available formats, either as Python pickle files, which provide quick read access with RDKit version 2022.09.5 or later, and as text-based SDF files with associated metadata in JSON format. Each file is named based on its amino acid sequence, with residues separated by periods, using standard one-letter codes with lowercase letters representing D-amino acids and "Me" prefixes representing <em>N</em>-methylated amino acids. The sequences are in no particular order, e.g., "C.R.E.M.P" and "R.E.M.P.C" correspond to the same peptide macrocycle. The filename extensions are ".pickle", ".sdf", and ".json".</p> <p>Each file in the “pickle” folder contains a Python dictionary with amino acid sequence, SMILES, CREST metadata, and a single RDKit molecule object containing all conformers. All files in the folder were compressed into a single “pickle.tar.gz” archive. In the “sdf_and_json” folder, each individual SDF file contains all conformers, each associated with its own JSON file that contains CREST metadata. Similarly, all are compressed into another single archive, “sdf_and_json.tar.bz2”. A single summary CSV file is also provided containing ”sequence”, “smiles”, “num_monomers”, “num_atoms”, “num_heavy_atoms”, along with the CREST metadata “totalconfs”, “uniqueconfs”, “lowestenergy”, “poplowestpct”, “temperature”, “ensembleenergy”, “ensembleentropy”, and “ensemblefreeenergy”. The number of unique conformers with different 3D structures is given by “uniqueconfs”, while “totalconfs” includes the number of rotamers in addition.</p> <p>The unzipped sizes of the archives are approximately 32 GB for "pickle.tar.gz" and 210 GB for "sdf_and_json.tar.bz2". If you encounter errors when trying to load the pickle files, please make sure your RDKit version is at least 2022.09.5. If that doesn't work, try other Python versions.</p>
Unveiling metastable ensembles of GRB2 and the relevance of interdomain communication during folding - available data.
<p>The folding process of multidomain proteins is a highly intricate phenomenon involving the assembly of distinct domains into a functional three-dimensional structure. During this process, each domain may fold independently while interacting with other domains to form a functional protein. The folding of multidomain proteins can be influenced by various factors, including the composition and structure of each domain or the presence of disordered linker regions, as well as the surrounding environment. Misfolding of multidomain proteins can lead to the formation of non-functional structures associated with a range of diseases, including cancers and neurodegenerative disorders. Understanding this process is an essential step for many biophysical analyzes, such as stability, interaction, malfunctioning, and rational drug design. One such multidomain protein is the growth factor receptor-bound protein 2 (GRB2), an adaptor protein essential in regulating cell survival. GRB2 consists of one central Src Homology 2 (SH2) domain flanked by two Src Homology 3 (SH3) domains. The SH2 domain interacts with phosphotyrosine regions in other proteins, while the SH3 domains recognize proline-rich regions on protein partners during cell signaling. In this study, we combined computational and experimental techniques to investigate the folding process of GRB2. We sampled the conformational space through computational simulations and mapped the mechanisms involved by calculating free energy profiles, indicating possible intermediate states. From the molecular dynamics and trajectories, we used the Energy Landscape Visualization Method (ELViM), which allowed us to visualize a three-dimensional representation of the overall energy surface. We identified two possible parallel folding routes that cannot be seen in a one-dimensional analysis, with one occurring more frequently during folding. Supporting these results, we used DSC and fluorescence spectroscopy techniques to confirm these intermediate states in vitro. Finally, we analyzed the deletion of domains to compare our model outputs with previously published results, supporting the presence of interdomain modulation. Overall, our study highlights the significance of interdomain communication within the GRB2 protein and its impact on the formation, stability, and structural plasticity, which are crucial for its interaction with other proteins in key signaling pathways.</p> <p> </p>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</p>
Data from: Calculating global annual methane increases from satellite data using an ensemble dynamic linear model approach
<p><em><strong>NOTE: This is no official S5P/TROPOMI WFMD XCH4 L3-Dataset.</strong></em></p> <p>This data is used and created by the example code provided in <a href="http://www.doi.org/10.5281/zenodo.8178927">10.5281/zenodo.8178927</a>, which is a supplement to the manuscript <em>'Zonal variability of methane trends derived from satellite data' </em>(Hachmeister et al., 2024 ; 10.5194/acp-24-577-2024). This data can be downloaded to skip the gridding step in the mentioned example code, to avoid downloading the complete input data.</p>
Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"
<p>The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.</p>
Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.
<p>Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
<p><strong>General Description</strong></p> <p>The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the <a href="https://zenodo.org/record/8392976">95th percentile of the monthly aggregated FAPAR</a> derived from <a href="http://glass.umd.edu/Overview.html">250 m 8 d GLASS V6 FAPAR</a>. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at <a href="http://github.com/Open-Earth-Monitor/Global_FAPAR_250m">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m</a>. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2021 - December 2021</li> <li><strong>Type of data: </strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR</li> <li><strong>Statistical methods used: </strong>Ensemble machine learning</li> <li><strong>Limitations or exclusions in the data: </strong>The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size: </strong>172,800 x 71,698</li> <li><strong>File format: </strong>Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p> </p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination: </strong>eml = ensemble machine learning</li> <li><strong>Position in the probability distribution / variable type:</strong> m = mean</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference: </strong>s = surface</li> <li><strong>Time reference begin time:</strong> 20210101 = 2021-01-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2021-12-31</li> <li><strong>Bounding box: </strong>go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230924 = 2023-09-24 (creation date)</li> </ol>
Dataset for "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6"
<p>This data set provides processed model output of ISMIP6 Greenland projections as documented and analysed in the following publication:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>About the data:<br> - The results are based on model output regridded conservatively to a 5x5 km regular ISMIP6 grid unless this is already the native grid. <br> - The results are calculated over the ice-covered area of Greenland, map projection error corrected, ice sheet model specific densities taken into account.<br> - The contribution of peripheral glaciers and ice caps has been removed, by considering their area-coverage in each grid cell.<br> - The results for the projections 'exp*' are all calculated as differences to the control experiment ctrl_proj (suffix cr in filename for control removed).<br> - Results for ctrl_proj and historical are un-corrected (no suffix cr in filename).</p> <p><br> Directory structure:<br> versionid<br> groupname1<br> modelname1<br> expid<br> scalars_mm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_rm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_zm_cr_GIS_groupname1_modelname1_expid.nc<br> ...</p> <p>Variables per file:</p> <p>scalars_mm_cr_GIS ----------------- Greenland wide numbers </p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> iarea - Fraction of grid cell covered by land ice [1]<br> iareagr - Fraction of grid cell covered by grounded ice sheet<br> iareafl - Fraction of grid cell covered by ice sheet flowing over seawater</p> <p>ivol - ice volume [m3]<br> ivolgr - grounded ice volume [m3]<br> ivolfl - floating ice volume [m3]<br> ivaf - ice volume above flotation [m3]</p> <p>lim - ice mass [kg]<br> limgr - grounded ice mass [kg]<br> limfl - floating ice mass [kg]<br> limaf - ice mass above flotation [kg]</p> <p>sle - sea-level equivalent mass [m] !! decreases with mass loss !! <br> smb - spatially integrated surface mass balance anomaly [kg s-1]</p> <p><br> scalars_rm_cr_GIS ----------------- IMBIE2-Rignot basins xx=[no,ne,se,sw,cw,nw]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p><br> scalars_zm_cr_GIS ----------------- IMBIE2-Zwally basins xx=[z11,z12,z13,z14,z21,z22,z31,z32,z33,z41,z42,z43,z50,z61,z62,z71,z72,z81,z82]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p> </p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p>
Verifying Spatial Structure in Ensembles of Forecast Fields
<p>Simulation results and GEFS data used in Jacobson et al. (2020). Please see the accompanying code <a href="https://github.com/joshhjacobson">repository</a> for further detail.</p>
MD Data for Patterns in protein flexibility: a comparison of NMR "ensembles", MD trajectories and crystallographic B-factors
<p>This data set comprises five zipped directories that contain the scripts and intermediate molecular dynamics (MD) results used in (initially as of April 24, 2017, updated with additional directories on December 15, 2020) a soon to be submitted paper, "Patterns in protein flexibility: a comparison of NMR 'ensembles', MD trajectories and crystallographic B-factors" written by the authors of this entry. An earlier version of this paper is available via BioRxiv, DOI: https://doi.org/10.1101/240655.</p> <p>This paper explores patterns in coordinate variance and coordinate uncertainty in MD trajectories and in protein structures derived from NMR and compares coordinate variances/uncertainties with those crystallographic B-factors. The files, MD_data.zip and MD_data2.zip, each unzip to contain input files and scripts for reproducing the MD trajectories used in this paper (using DESMOND): MD_data.zip contains input files/scripts for the MD trajectories used in the preprint; MD_data2.zip contains input files/scripts for trajectories ran following publication of the preprint. The file btab_analysis_scripts.zip contains key scripts for analyzing those trajectories (following file conversion with VMD and superimposition with THESEUS) in MATLAB (this analysis assumes the presence of the FindCore Toolbox, written by David Snyder and available via the MATLAB Central File Exchange, as well as the MATLAB Statistics and Machine Learning Toolbox). And the files, superimposed_MD_trajectories.zip and superimposed_MD_trajectories2.zip, each unzip to yield the trajectories (superimposed using THESEUS and in PDB multimodel file format) analyzed in the soon to be submitted paper: superimposed_MD_trajectories.zip contains trajectories reported in the preprint and superimposed_MD_trajectories2.zip contains the results of subsequent simulations. </p>
Ensemble mean of CMIP5 TOS, for the period 1971 to 2000
<p>Ensemble mean of the variable TOS (temperature of surface, i.e. Sea Surface Temperature), from CMIP5 control run.</p> <p>Data are independant of any RCP (before 2006), and can thus be used for computing an SST climatology.</p>
Supplementary Material: Conformational Ensemble of the Poliovirus 3CD Precursor Observed by MD Simulations and Confirmed by SAXS: A Strategy to Expand the Viral Proteome?
<p>Supplementary video for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(11), 5962-5986; doi:10.3390/v7112919; http://www.mdpi.com/1999-4915/7/11/2919.</p> <p><strong>Movie S1.</strong> Dynamic interface between 3C and 3D domains revealed by accelerated MD. The 3C and 3D domains are colored cyan and blue, respectively. The active-site residues of the protease (His-40, Glu-71, Cys-147) and the polymerase (Asp-416, Asp-511, Asp-512) domains are represented by red spheres to help identifying the relative orientations of two domains.</p>
Ensemble Metatone 2013 Rehearsal Study Performance Data
<p>This repository contains free-improvised performances by percussion/computer group, Ensemble Metatone. This series of rehearsals-as-research was performed to investigate percussive interactions with touch-screen computer music instruments developed by Charles Martin.</p> <p>These performances were recorded with multiple channels of audio and video. Touch-screen data from the computer music instruments was also recorded and animated to help understand the performers’ touch gestures throughout the performance.</p> <p>The video of each performance as well as interaction log data have been archived in this repository.</p> <p>The performances were free-improvised with no instructions given to any of the performers. The end of the improvisation was defined to be when all performers had stopped playing.</p> <p>The sessions took place between April and August 2013 in the ANU School of Music Listening Space studio and Band Room concert hall.</p> <p><strong>Apps:</strong></p> <p>The two iPad apps used in this study were MetaTravels (doi: 10.5281/zenodo.50705) and MetaLonsdale (doi: 10.5281/zenodo.50716).</p> <p>These apps are further discussed in the references below.</p> <p><strong>References:</strong></p> <p>This rehearsal-as-research series has been discussed in more depth in the following publications.</p> <p>C. Martin, H. Gardner, and B. Swift. Exploring percussive gesture on iPads with Ensemble Metatone. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’14, pages 1025–1028, New York, NY, USA, 2014. ACM. doi: 10.1145/2556288.2557226</p> <p>C. Martin. Making improvised music for iPad and percussion with Ensemble Metatone. In T. Opie, editor, Proceedings of the Australasian Computer Music Conference, ACMC 2014, pages 115–118, Fitzroy, Australia, 2014. Australasian Computer Music Association. http://hdl.handle.net/1885/95314</p> <p>C. Martin, H. Gardner, and B. Swift. MetaTravels and MetaLonsdale: iPad apps for percussive impro- visation. In CHI ’14 Extended Abstracts on Human Factors in Computing Systems, CHI EA ’14, pages 547–550, New York, NY, USA, 2014. ACM. doi: 10.1145/2559206.2574805</p> <p>C. Martin and H. Gardner. A percussion-focussed approach to preserving touch-screen improvisation. In D. England, T. Schiphorst, and N. Bryan-Kinns, editors, Curating the Digital: Spaces for Art and Interaction, Springer Series on Cultural Computing. Springer International Publishing, Switzerland, 2016. DOI: 10.1007/978–3–319–28722–5</p> <p><strong>Copyright</strong></p> <p>These data are released for free use under the Artistic License 2.0, however, the performers retain Copyright and moral right over their original improvised performances.</p>
Neural Touch-Screen Ensemble Performance 2017-07-03
<p>A studio performance of an RNN-controlled Touch Screen Ensemble from 2017-07-03 at the University of Oslo.</p> <p>In this performance, a touch-screen musician improvises with a computer-controlled ensemble of three artificial performers. A recurrent neural network tracks the touch gestures of the human performer and predicts musically appropriate gestural responses for the three artificial musicians. The performances on the three 'AI' iPads are then constructed from matching snippets of previous human recordings. A plot of the whole ensemble's touch gestures are shown on the projected screen.</p> <p>This performance uses Metatone Classifier (https://doi.org/10.5281/zenodo.51712) to track touch gestures and Gesture-RNN (https://github.com/cpmpercussion/gesture-rnn) to predict gestural states for the ensemble. The touch-screen app used in this performance was PhaseRings (https://doi.org/10.5281/zenodo.50860).</p>
Videomitschnitte des Symposiumskonzerts «Rubebe, rubechette e rubecone – Alte und Neue Musik für fellbespannte Streichinstrumente, Gesang, Harfe, Laute und Perkussion» mit dem ensemble arcimboldo (29.4.2023)
<p>Das mit einem Tierfell als Decke bespannte Rabab zählte neben der Fidel – und dem erst ab dem 14. Jahrhundert belegten Rebec – zu den wichtigsten Streichinstrumenten des Mittelalters und der frühen Renaissance. Umso erstaunlicher ist die Diskrepanz zwischen den zahlreichen historischen Quellen und ihrer fehlenden ‹Resonanz› in Musikwissenschaft und Musikpraxis. Aus diesen Gründen wurden Rabab und Rebec von 2019–2023 in einem Forschungsprojekt des Schweizerischen Nationalfonds an der Hochschule der Künste Bern HKB interdisziplinär untersucht und die Ergebnisse im April 2023 an einem internationalen Symposium präsentiert. Ein wichtiger Meilenstein des Forschungsprojekts ist das Symposiumskonzert des ensemble arcimboldo. Hier erklingen erstmals die im Forschungsprojekt rekonstruierten Rabab-Prototypen.</p><p>Der besondere Klang des fellbespannten mittelalterlichen Streichinstruments Rabab wird beim ersten Hören oft als ‹orientalisch› bezeichnet. Obwohl der Ursprung des Instruments in al Andalus, jenem ab dem 8. Jahrhundert von Muslimen besetzten Teil der Iberischen Halbinsel lag, verbreitete es sich vom 13. bis zum 15. von der Iberischen Halbinsel aus nach Frankreich und Italien. Diesen Weg des Rabab als klanglicher ‹Botschafter› zwischen den Kulturen und musikalischen Stilen spiegelt das Konzertprogramm wider: mit Cantigas de Santa Maria, marokkanischer andalusi-Musik, Werken aus dem Squarcialupi-Codex, Improvisationen sowie der Uraufführung zweier zeitgenössischer Kompositionen von Eleni Ralli (*1984) und Abril Padilla (*1970).</p><p>ensemble arcimboldo, Basel <br>Grace Newcombe – Sopran, Harfe <br>Félix Verry – Alt-Rabab, Fidel <br>Thilo Hirsch – Tenor-Rabab, Bass-Rabab, Tenor <br>Leonardo Bortolotto – Bass-Rabab <br>Peppe Frana – Plektrumlaute <br>Titus Bellwald – Tar</p><p>Wir danken dem Bernischen Historischen Museum als Gastgeber sowie folgenden Stiftungen für die grosszügige Unterstützung der Kompositionsaufträge, des Konzerts und der Erstellung der Video-Mitschnitte: Schweizerischer Nationalfonds, Fachausschuss Musik BS/BL, Gesellschaft zu Schuhmachern Bern, Burgergemeinde Bern, Schweizerische Interpretenstiftung SIS.</p><p>Live-Aufnahme am 29.4.2023 im Orientalischen Saal des Bernischen Historischen Museums, Audio- und Videoproduktion: Oren Kirschenbaum.</p><p>Siehe auch: <a href="https://youtube.com/playlist?list=PL5J-BZoNMhGL2qSFYgLQ_8oMwkjSRIc3v&si=-T_SakuAKNOilc6u">https://youtube.com/playlist?list=PL5J-BZoNMhGL2qSFYgLQ_8oMwkjSRIc3v&si=-T_SakuAKNOilc6u</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.